scispace - formally typeset
Search or ask a question
Topic

Harmonic wavelet transform

About: Harmonic wavelet transform is a research topic. Over the lifetime, 9602 publications have been published within this topic receiving 247336 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: Application of the proposed despeckling method on real diagnostic ultrasound images has shown a clear improvement over other methods and is compared on the basis of signal to mean square error (SMSE) and signal to noise ratio (SNR).

98 citations

Journal ArticleDOI
TL;DR: A fast pattern matching algorithm with a large set of templates based on the typical template matching speeded up by the dual decomposition; the Fourier transform and the Karhunen-Loeve transform that is appropriate for the search of an object with unknown distortion within a short period.
Abstract: We present a fast pattern matching algorithm with a large set of templates. The algorithm is based on the typical template matching speeded up by the dual decomposition; the Fourier transform and the Karhunen-Loeve transform. The proposed algorithm is appropriate for the search of an object with unknown distortion within a short period. Patterns with different distortion differ slightly from each other and are highly correlated. The image vector subspace required for effective representation can be defined by a small number of eigenvectors derived by the Karhunen-Loeve transform. A vector subspace spanned by the eigenvectors is generated, and any image vector in the subspace is considered as a pattern to be recognized. The pattern matching of objects with unknown distortion is formulated as the process to extract the portion of the input image, find the pattern most similar to the extracted portion in the subspace, compute normalized correlation between them at each location in the input image, and find the location with the best score. Searching for objects with unknown distortion requires vast computation. The formulation above makes it possible to decompose highly correlated reference images into eigenvectors, as well as to decompose images in frequency domain, and to speed up the process significantly.

98 citations

Journal ArticleDOI
TL;DR: This letter has found that using the wavelet transform in time and space, combined with a multiresolution approach, leads to an efficient and effective method of compression.
Abstract: This letter present results on using wavelet transforms in both space and time for compression of real time digital video data. The advantages of the wavelet transform for static image analysis are well known.2 We have found that using the wavelet transform in time and space, combined with a multiresolution approach, leads to an efficient and effective method of compression. In addition, the computational requirements are considerably less than for other compression methods, and are more suited to VLSI implementation. Some preliminary results of compression on a sample video will be presented.

97 citations

Journal ArticleDOI
TL;DR: The proposed wavelet transform generalizes the Haar-like transform recently introduced by Gavish, and can also construct data adaptive orthonormal wavelets beyond Haar, and is applied to the data using a modified version of the common one-dimensional wavelet filtering and decimation scheme.
Abstract: In this paper we propose a new wavelet transform applicable to functions defined on high dimensional data, weighted graphs and networks. The proposed method generalizes the Haar-like transform recently introduced by Gavish , and can also construct data adaptive orthonormal wavelets beyond Haar. It is defined via a hierarchical tree, which is assumed to capture the geometry and structure of the input data, and is applied to the data using a modified version of the common one-dimensional (1D) wavelet filtering and decimation scheme. The adaptivity of this wavelet scheme is obtained by permutations derived from the tree and applied to the approximation coefficients in each decomposition level, before they are filtered. We show that the proposed transform is more efficient than both the 1D and two-dimension 2D separable wavelet transforms in representing images. We also explore the application of the proposed transform to image denoising, and show that combined with a subimage averaging scheme, it achieves denoising results which are similar to those obtained with the K-SVD algorithm.

97 citations

Journal ArticleDOI
TL;DR: A new hybrid method for image approximation is proposed that exploits the advantages of the usual tensor product wavelet transform for the representation of smooth images and uses the EPWT for an efficient representation of edges and texture.
Abstract: The easy path wavelet transform (EPWT) has recently been proposed by one of the authors as a tool for sparse representations of bivariate functions from discrete data, in particular from image data. The EPWT is a locally adaptive wavelet transform. It works along pathways through the array of function values and exploits the local correlations of the given data in a simple appropriate manner. However, the EPWT suffers from its adaptivity costs that arise from the storage of path vectors. In this paper, we propose a new hybrid method for image approximation that exploits the advantages of the usual tensor product wavelet transform for the representation of smooth images and uses the EPWT for an efficient representation of edges and texture. Numerical results show the efficiency of this procedure.

97 citations


Network Information
Related Topics (5)
Image processing
229.9K papers, 3.5M citations
82% related
Feature extraction
111.8K papers, 2.1M citations
82% related
Image segmentation
79.6K papers, 1.8M citations
81% related
Support vector machine
73.6K papers, 1.7M citations
80% related
Feature (computer vision)
128.2K papers, 1.7M citations
78% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202323
202274
20213
20207
20196
201831